{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled15.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "2BUADPhcMtJE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install allennlp\n",
        "!pip install fairseq"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kS0bymCszWqe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "!ls \"/content/drive/My Drive/\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Y9rtTVZF4is",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_cols={'toxic','severe_toxic','obscene','threat','insult','identity_hate'}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DnW9JMgO3-7d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch.optim as optim\n",
        "from typing import *\n",
        "from overrides import overrides\n",
        "\n",
        "from allennlp.data import Instance\n",
        "from allennlp.data.dataset_readers import DatasetReader\n",
        "from allennlp.data.fields import *\n",
        "from allennlp.data.token_indexers import SingleIdTokenIndexer\n",
        "from allennlp.data.tokenizers import Token\n",
        "from allennlp.data.token_indexers import TokenIndexer\n",
        "\n",
        "class Config(dict):\n",
        "    def __init__(self,**kwargs):\n",
        "        super().__init__(**kwargs)\n",
        "        for k,v in kwargs.items():\n",
        "            setattr(self,k,v)\n",
        "    def set(self,key,val):\n",
        "        self[key] = val\n",
        "        setattr(self,key,value)\n",
        "\n",
        "config = Config(testing=True,\n",
        "               seed=1,\n",
        "               batch_size = 64,\n",
        "               lr=3e-4,\n",
        "               epochs=1000,\n",
        "               hidden_size = 64,\n",
        "               max_seq_len = 100,\n",
        "               max_vocab_size=10000,)\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mIOBdeT-C6Yo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "DATA_PATH = \"/content/drive/My Drive/jigsaw/\"\n",
        "USE_CUDA = torch.cuda.is_available()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYKZKOaeDL6K",
        "colab_type": "code",
        "outputId": "94284f01-c287-4d17-d0fb-dc3e5f59507f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "torch.manual_seed(config.seed)"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7fea53b2f210>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wZqEOVh8CXLX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class JigsawDataReader(DatasetReader):\n",
        "    def __init__(self,tokenizer,token_indexers,max_seq_len=config.max_seq_len):\n",
        "        super().__init__(lazy=False)\n",
        "        self.tokenizer = tokenizer\n",
        "        self.token_indexers = token_indexers or {\"tokens\":SingleIdTokenIndexer()}\n",
        "        self.max_seq_len = max_seq_len\n",
        "    \n",
        "    @overrides\n",
        "    def text_to_instance(self,token,id,labels):\n",
        "        sentence_field = TextField(token,self.token_indexers)\n",
        "        fields = {'tokens':sentence_field}\n",
        "        \n",
        "        id_field = MetadataField(id)\n",
        "        fields['id'] = id_field\n",
        "        \n",
        "        if labels is None:\n",
        "            labels = np.zeros(len(label))\n",
        "        label_field = ArrayField(array=labels)\n",
        "        fields['label'] = label_field\n",
        "        return Instance(fields)\n",
        "            \n",
        "        \n",
        "    @overrides\n",
        "    def _read(self,path=DATA_PATH):\n",
        "        df = pd.read_csv(path)\n",
        "        if config.testing == True:df = df.head(1000)\n",
        "        for i,row in df.iterrows():\n",
        "            yield self.text_to_instance([Token(x) for x in self.tokenizer(row[\"comment_text\"])],\n",
        "                                       row[\"id\"],row[label_cols].values)\n",
        "        "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QSqWR5C3DK_-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from allennlp.data.tokenizers.word_splitter import SpacyWordSplitter\n",
        "\n",
        "token_indexers = SingleIdTokenIndexer()\n",
        "\n",
        "def tokenizer(x):\n",
        "    return [w.text for w in SpacyWordSplitter(language='en_core_web_sm',pos_tags=False).split_words(x)[:config.max_seq_len]]\n",
        "\n",
        "reader = JigsawDataReader(tokenizer=tokenizer,token_indexers={'tokens':token_indexers})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XMV0AQdruCXK",
        "colab_type": "code",
        "outputId": "3be0f8e4-ad86-40ee-fa70-67da5fc8a423",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "train_ds,test_ds = (reader.read(DATA_PATH+w) for w in [\"train.csv\", \"test_proced.csv\"])\n",
        "val_ds = None"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "267it [00:00, 513.64it/s]\n",
            "251it [00:00, 402.23it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F3CAziV863_b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vars(train_ds[0].fields['tokens'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JPDXLrIcvh_v",
        "colab_type": "code",
        "outputId": "c5ddf688-ea74-4c5a-c739-4468ee31a320",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from allennlp.data.vocabulary import Vocabulary\n",
        "vocab = Vocabulary.from_instances(train_ds,max_vocab_size = config.max_vocab_size)"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 267/267 [00:00<00:00, 12654.14it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bkv7F2n98vwq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from allennlp.data.iterators import BucketIterator\n",
        "iterator = BucketIterator(batch_size=config.batch_size,sorting_keys=[('tokens','num_tokens')],)\n",
        "\n",
        "iterator.index_with(vocab)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mWhdKTKK-vbD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch = next(iter(iterator(train_ds)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pn9FRSaO-1Ee",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from allennlp.modules.seq2vec_encoders import Seq2VecEncoder,PytorchSeq2VecWrapper\n",
        "from allennlp.modules.text_field_embedders import TextFieldEmbedder\n",
        "from allennlp.nn.util import get_text_field_mask\n",
        "from allennlp.models import Model\n",
        "\n",
        "class BaslinModel(Model):\n",
        "    def __init__(self,word_embeddings,encoder,out_sz=len(label_cols)):\n",
        "        super().__init__(vocab)\n",
        "        self.word_embeddings = word_embeddings\n",
        "        self.encoder = encoder\n",
        "        self.projection = nn.Linear(self.encoder.get_output_dim(),out_sz)\n",
        "        self.loss = nn.BCEWithLogitsLoss()\n",
        "    \n",
        "    def forward(self,tokens,id,label):\n",
        "        mask = get_text_field_mask(tokens)\n",
        "        embeddings = self.word_embeddings(tokens)\n",
        "        state = self.encoder(embeddings,mask)\n",
        "        class_logits = self.projection(state)       \n",
        "        out = {'class_logits':class_logits}\n",
        "        out['loss'] = self.loss(class_logits,label)\n",
        "        return out\n",
        "   "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O4f4sfiaI1Ua",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from allennlp.modules.token_embedders import Embedding\n",
        "from allennlp.modules.text_field_embedders import BasicTextFieldEmbedder\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "paEUGTD3JmVT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "token_embedding = Embedding(num_embeddings=config.max_vocab_size+2,embedding_dim=300,padding_index=0)\n",
        "word_embeddings = BasicTextFieldEmbedder({'tokens':token_embedding})\n",
        "encoder = PytorchSeq2VecWrapper(nn.LSTM(word_embeddings.get_output_dim(),config.batch_size,bidirectional=True,batch_first = True))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6aR6erf1K8Cy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = BaslinModel(word_embeddings,encoder)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJhNhKEELxzE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if USE_CUDA:model.cuda()\n",
        "else:\n",
        "    model()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9qXzbJdrMCDP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import allennlp.nn.util as nn_util"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6OkrFFiMMJD3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch = nn_util.move_to_device(batch,0 if USE_CUDA else -1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6KeaQoVsMjIk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tokens = batch['tokens']\n",
        "label = batch['label']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ck1x2JwoNVe9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mask = get_text_field_mask(tokens)\n",
        "embeddings = model.word_embeddings(tokens)\n",
        "state = model.encoder(embeddings, mask)\n",
        "class_logits = model.projection(state)\n",
        "class_logits"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NcAhjezuO7do",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loss = model(**batch)[\"loss\"]\n",
        "loss.backward()\n",
        "optimizer = optim.Adam(model.parameters(),config.lr)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JAwAJOowPvPB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from allennlp.training.trainer import Trainer\n",
        "trainer = Trainer(\n",
        "            model=model,\n",
        "            optimizer = optimizer,\n",
        "            iterator=iterator,\n",
        "            train_dataset=train_ds,\n",
        "            cuda_device=0 if USE_CUDA else -1,\n",
        "            num_epochs=config.epochs,)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P0YyyFCaRZly",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "metrics = trainer.train()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kMRIMVfPSivA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from allennlp.data.iterators import DataIterator\n",
        "from tqdm import tqdm\n",
        "from scipy.special import expit"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ltVVHCFVs80",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def tonp(tsr):return tsr.detach().cpu().numpy()\n",
        "\n",
        "class Predictor:\n",
        "    def __init__(self,model,iterator,device=-1):\n",
        "        self.model = model\n",
        "        self.iterator = iterator\n",
        "        self.device = device\n",
        "    \n",
        "    def _extract_data(self,batch):\n",
        "        out_dict = model(**batch)\n",
        "        return expit(tonp(out_dict['class_logits']))\n",
        "        \n",
        "        \n",
        "    def predict(self,ds):\n",
        "        pred_generator = self.iterator(ds,num_epochs=1,shuffle=False)\n",
        "        self.model.eval()\n",
        "        pred_generator_tqdm = tqdm(pred_generator,\n",
        "                                   total=self.iterator.get_num_batches(ds))\n",
        "        preds = []\n",
        "        with torch.no_grad():\n",
        "            for batch in pred_generator_tqdm:\n",
        "                batch = nn_util.move_to_device(batch, self.device)\n",
        "                preds.append(self._extract_data(batch))\n",
        "        return np.concatenate(preds, axis=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0zZvIspVt2p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from allennlp.data.iterators import BasicIterator\n",
        "seq_iterator = BasicIterator(batch_size=64)\n",
        "seq_iterator.index_with(vocab)\n",
        "predictor = Predictor(model, seq_iterator, device=0 if USE_CUDA else -1)\n",
        "train_preds = predictor.predict(train_ds) \n",
        "test_preds = predictor.predict(test_ds)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2oLZ3rWoYxOO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "1293542a-e699-4398-f76c-01feeecf1883"
      },
      "source": [
        "# from allennlp.predictors.sentence_tagger import SentenceTaggerPredictor\n",
        "# tagger = SentenceTaggerPredictor(model, reader)\n",
        "# tagger.predict(\"this tutorial was great!\")\n",
        "# tagger.predict(\"this tutorial was horrible!\")\n",
        "train_preds"
      ],
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2.11230545e-05, 3.05950266e-06, 3.37090000e-06, 5.10661048e-05,\n",
              "        1.94674667e-05, 1.77881338e-05],\n",
              "       [2.15447162e-05, 3.02685339e-06, 3.29940485e-06, 4.90051898e-05,\n",
              "        1.89136190e-05, 1.66517548e-05],\n",
              "       [1.61007010e-05, 2.53492613e-06, 3.33250773e-06, 5.96500213e-05,\n",
              "        1.65580404e-05, 1.57704053e-05],\n",
              "       ...,\n",
              "       [2.11578936e-05, 3.02071112e-06, 3.25547830e-06, 4.80166636e-05,\n",
              "        1.89544444e-05, 1.68089900e-05],\n",
              "       [1.71683405e-05, 2.61021273e-06, 3.17119202e-06, 4.92822546e-05,\n",
              "        1.67630624e-05, 1.49623065e-05],\n",
              "       [2.06936566e-05, 2.90289609e-06, 3.30349803e-06, 5.01570212e-05,\n",
              "        1.87473124e-05, 1.64891626e-05]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JsWm33xK1kD2",
        "colab_type": "code",
        "outputId": "64ea706e-f891-49bb-b5c7-0bef5b8a2af8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from typing import Iterator, List, Dict\n",
        "import torch\n",
        "import torch.optim as optim\n",
        "import numpy as np\n",
        "from allennlp.data import Instance\n",
        "from allennlp.data.fields import TextField, SequenceLabelField\n",
        "from allennlp.data.dataset_readers import DatasetReader\n",
        "from allennlp.common.file_utils import cached_path\n",
        "from allennlp.data.token_indexers import TokenIndexer, SingleIdTokenIndexer\n",
        "from allennlp.data.tokenizers import Token\n",
        "from allennlp.data.vocabulary import Vocabulary\n",
        "from allennlp.models import Model\n",
        "from allennlp.modules.text_field_embedders import TextFieldEmbedder, BasicTextFieldEmbedder\n",
        "from allennlp.modules.token_embedders import Embedding\n",
        "from allennlp.modules.seq2seq_encoders import Seq2SeqEncoder, PytorchSeq2SeqWrapper\n",
        "from allennlp.nn.util import get_text_field_mask, sequence_cross_entropy_with_logits\n",
        "from allennlp.training.metrics import CategoricalAccuracy\n",
        "from allennlp.data.iterators import BucketIterator\n",
        "from allennlp.training.trainer import Trainer\n",
        "from allennlp.predictors import SentenceTaggerPredictor\n",
        "\n",
        "torch.manual_seed(1)\n",
        "class PosDatasetReader(DatasetReader):\n",
        "    \"\"\"\n",
        "    DatasetReader for PoS tagging data, one sentence per line, like\n",
        "\n",
        "        The###DET dog###NN ate###V the###DET apple###NN\n",
        "    \"\"\"\n",
        "    def __init__(self, token_indexers: Dict[str, TokenIndexer] = None) -> None:\n",
        "        super().__init__(lazy=False)\n",
        "        self.token_indexers = token_indexers or {\"tokens\": SingleIdTokenIndexer()}\n",
        "        \n",
        "    def text_to_instance(self, tokens: List[Token], tags: List[str] = None) -> Instance:\n",
        "        sentence_field = TextField(tokens, self.token_indexers)\n",
        "        fields = {\"sentence\": sentence_field}\n",
        "\n",
        "        if tags:\n",
        "            label_field = SequenceLabelField(labels=tags, sequence_field=sentence_field)\n",
        "            fields[\"labels\"] = label_field\n",
        "\n",
        "        return Instance(fields)\n",
        "      \n",
        "    def _read(self, file_path: str) -> Iterator[Instance]:\n",
        "        with open(file_path) as f:\n",
        "            for line in f:\n",
        "                pairs = line.strip().split()\n",
        "                sentence, tags = zip(*(pair.split(\"###\") for pair in pairs))\n",
        "                yield self.text_to_instance([Token(word) for word in sentence], tags)\n",
        "                \n",
        "class LstmTagger(Model):\n",
        "    def __init__(self,\n",
        "                 word_embeddings: TextFieldEmbedder,\n",
        "                 encoder: Seq2SeqEncoder,\n",
        "                 vocab: Vocabulary) -> None:\n",
        "        super().__init__(vocab)\n",
        "        self.word_embeddings = word_embeddings\n",
        "        self.encoder = encoder\n",
        "        self.hidden2tag = torch.nn.Linear(in_features=encoder.get_output_dim(),\n",
        "                                          out_features=vocab.get_vocab_size('labels'))\n",
        "        self.accuracy = CategoricalAccuracy()\n",
        "    def forward(self,\n",
        "                sentence: Dict[str, torch.Tensor],\n",
        "                labels: torch.Tensor = None) -> Dict[str, torch.Tensor]:\n",
        "        mask = get_text_field_mask(sentence)\n",
        "        embeddings = self.word_embeddings(sentence)\n",
        "        encoder_out = self.encoder(embeddings, mask)\n",
        "        tag_logits = self.hidden2tag(encoder_out)\n",
        "        output = {\"tag_logits\": tag_logits}\n",
        "        if labels is not None:\n",
        "            self.accuracy(tag_logits, labels, mask)\n",
        "            output[\"loss\"] = sequence_cross_entropy_with_logits(tag_logits, labels, mask)\n",
        "\n",
        "        return output\n",
        "    def get_metrics(self, reset: bool = False) -> Dict[str, float]:\n",
        "        return {\"accuracy\": self.accuracy.get_metric(reset)}\n",
        "    \n",
        "reader = PosDatasetReader()\n",
        "train_dataset = reader.read(cached_path(\n",
        "    'https://raw.githubusercontent.com/allenai/allennlp'\n",
        "    '/master/tutorials/tagger/training.txt'))\n",
        "validation_dataset = reader.read(cached_path(\n",
        "    'https://raw.githubusercontent.com/allenai/allennlp'\n",
        "    '/master/tutorials/tagger/validation.txt'))\n",
        "vocab = Vocabulary.from_instances(train_dataset + validation_dataset)\n",
        "EMBEDDING_DIM = 6\n",
        "HIDDEN_DIM = 6\n",
        "token_embedding = Embedding(num_embeddings=vocab.get_vocab_size('tokens'),\n",
        "                            embedding_dim=EMBEDDING_DIM)\n",
        "word_embeddings = BasicTextFieldEmbedder({\"tokens\": token_embedding})\n",
        "lstm = PytorchSeq2SeqWrapper(torch.nn.LSTM(EMBEDDING_DIM, HIDDEN_DIM, batch_first=True))\n",
        "model = LstmTagger(word_embeddings, lstm, vocab)\n",
        "if torch.cuda.is_available():\n",
        "    cuda_device = 0\n",
        "    model = model.cuda(cuda_device)\n",
        "else:\n",
        "    cuda_device = -1\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.1)\n",
        "iterator = BucketIterator(batch_size=2, sorting_keys=[(\"sentence\", \"num_tokens\")])\n",
        "iterator.index_with(vocab)\n",
        "trainer = Trainer(model=model,\n",
        "                  optimizer=optimizer,\n",
        "                  iterator=iterator,\n",
        "                  train_dataset=train_dataset,\n",
        "                  validation_dataset=validation_dataset,\n",
        "                  patience=10,\n",
        "                  num_epochs=1000,\n",
        "                  cuda_device=cuda_device)\n",
        "trainer.train()\n",
        "predictor = SentenceTaggerPredictor(model, dataset_reader=reader)\n",
        "tag_logits = predictor.predict(\"The dog ate the apple\")['tag_logits']\n",
        "tag_ids = np.argmax(tag_logits, axis=-1)\n",
        "print([model.vocab.get_token_from_index(i, 'labels') for i in tag_ids])\n",
        "# Here's how to save the model.\n",
        "with open(\"/tmp/model.th\", 'wb') as f:\n",
        "    torch.save(model.state_dict(), f)\n",
        "vocab.save_to_files(\"/tmp/vocabulary\")\n",
        "# And here's how to reload the model.\n",
        "vocab2 = Vocabulary.from_files(\"/tmp/vocabulary\")\n",
        "model2 = LstmTagger(word_embeddings, lstm, vocab2)\n",
        "with open(\"/tmp/model.th\", 'rb') as f:\n",
        "    model2.load_state_dict(torch.load(f))\n",
        "if cuda_device > -1:\n",
        "    model2.cuda(cuda_device)\n",
        "predictor2 = SentenceTaggerPredictor(model2, dataset_reader=reader)\n",
        "tag_logits2 = predictor2.predict(\"The dog ate the apple\")['tag_logits']\n",
        "np.testing.assert_array_almost_equal(tag_logits2, tag_logits)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:21: DeprecationWarning: The linear_assignment_ module is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "['DET', 'NN', 'V', 'DET', 'NN']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xzYLuVP3VpwB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l2Fk0qou39X3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ynFT-Q7p_Qwq",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QJgqL_PdUuLB",
        "colab_type": "code",
        "outputId": "1616b362-1dca-48ba-f2df-d6830ecd0953",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "source": [
        "# 导入必要的库\n",
        "import torch\n",
        "from pytorch_transformers import GPT2Tokenizer, GPT2LMHeadModel\n",
        "# 加载预训练模型tokenizer (vocabulary)\n",
        "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
        "# 对文本输入进行编码\n",
        "text = \"What is the fastest car in the\"\n",
        "indexed_tokens = tokenizer.encode(text)\n",
        "# 在PyTorch张量中转换indexed_tokens\n",
        "tokens_tensor = torch.tensor([indexed_tokens])\n",
        "# 加载预训练模型 (weights)\n",
        "model = GPT2LMHeadModel.from_pretrained('gpt2')\n",
        "#将模型设置为evaluation模式，关闭DropOut模块\n",
        "model.eval()\n",
        "# 如果你有GPU，把所有东西都放在cuda上\n",
        "tokens_tensor = tokens_tensor.to('cuda')\n",
        "model.to('cuda')\n",
        "# 预测所有的tokens\n",
        "with torch.no_grad(): \n",
        "  outputs = model(tokens_tensor) \n",
        "  predictions = outputs[0]\n",
        "# 得到预测的单词\n",
        "  predicted_index = torch.argmax(predictions[0, -1, :]).item()\n",
        "  predicted_text = tokenizer.decode(indexed_tokens + [predicted_index])\n",
        "  # 打印预测单词\n",
        "  print(predicted_text)\n",
        "  #代码很直观，我们将文本标记为数字序列并将其索引，然后将其传递给GPT2LMHeadModel。"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-09d6c8e2b2ff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpytorch_transformers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mGPT2Tokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGPT2LMHeadModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;31m# 加载预训练模型tokenizer (vocabulary)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtokenizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGPT2Tokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'gpt2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# 对文本输入进行编码\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pytorch_transformers'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FjjKp8K6VMax",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}